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GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function

Open access
Mostafavi, Sara, Ray, Debajyoti, Warde-Farley, David, Grouios, Chris, Morris, Quaid·2008·Genome Biology
DOI 10.1186/gb-2008-9-s1-s4
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Abstract

Background: Most successful computational approaches for protein function prediction integrate multiple genomics and proteomics data sources to make inferences about the function of unknown proteins. The most accurate of these algorithms have long running times, making them unsuitable for real-time protein function prediction in large genomes. As a result, the predictions of these algorithms are stored in static databases that can easily become outdated. We propose a new algorithm, GeneMANIA, that is as accurate as the leading methods, while capable of predicting protein function in real-time. Results: We use a fast heuristic algorithm, derived from ridge regression, to integrate multiple functional association networks and predict gene function from a single process-specific network using label propagation. Our algorithm is efficient enough to be deployed on a modern webserver and is as accurate as, or more so than, the leading methods on the MouseFunc I benchmark and a new yeast function prediction benchmark; it is robust to redundant and irrelevant data and requires, on average, less than ten seconds of computation time on tasks from these benchmarks. Conclusion: GeneMANIA is fast enough to predict gene function on-the-fly while achieving state-of-the-art accuracy. A prototype version of a GeneMANIA-based webserver is available at http://morrislab.med.utoronto.ca/prototype.

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Research works printed but not linked: 31

  1. Stuart JM, Segal E, Koller D, Kim SK: A gene-coexpression network for global discovery of conserved genetic modules. Science. 2003, 302: 249-255. · DOI · Paper page
  2. Zhang LV, King OD, Wong SL, Goldberg DS, Tong AH, Lesage G, Andrews B, Bussey H, Boone C, Roth FP: Motifs, themes and thematic maps of an integrated Saccharomyces cerevisiae interaction network. J Biol. 2005, 4: 6- · DOI · Paper page
  3. Giaever G, Shoemaker DD, Jones TW, Liang H, Winzeler EA, Astromoff A, Davis RW: Genomic profiling of drug sensitivities via induced haploinsufficiency. Nat Genet. 1999, 21: 278-283. · DOI · Paper page
  4. von Mering C, Krause R, Snel B, Cornell M, Oliver SG, Fields S, Bork P: Comparative assessment of large-scale data sets of protein-protein interactions. Nature. 2002, 417: 399-403. · DOI · Paper page
  5. Polacco BJ, Babbitt PC: Automated discovery of three-dimensional motifs for protein function annotation. Bioinformatics. 2006, 22: 723-730. · DOI
  6. Pellegrini M, Marcotte EM, Thompson MJ, Eisenberg D, Yeates TO: Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc Natl Acad Sci USA. 1999, 96: 4285-4288. · DOI · Paper page
  7. Hegyi H, Gerstein M: The relationship between protein structure and function: a comprehensive survey with application to the yeast genome. J Mol Biol. 1999, 288: 147-164. · DOI · Paper page
  8. Lanckriet GR, Deng M, Cristianini N, Jordan MI, Noble WS: Kernel-based data fusion and its application to protein function prediction in yeast. Pac Symp Biocomput. 2004, 300-311.
  9. Walker MG, Volkmuth W, Sprinzak E, Hodgson D, Klingler T: Prediction of gene function by genome-scale expression analysis: prostate cancer-associated genes. Genome Res. 1999, 9: 1198-1203. · DOI · Paper page
  10. Wu LF, Hughes TR, Davierwala AP, Robinson MD, Stoughton R, Altschuler SJ: Large-scale prediction of Saccharomyces cerevisiae gene function using overlapping transcriptional clusters. Nat Genet. 2002, 31: 255-265. · DOI · Paper page
  11. Zhang W, Morris QD, Chang R, Shai O, Bakowski MA, Mitsakakis N, Mohammad N, Robinson MD, Zirngibl R, Somogyi E, Laurin N, Eftekharpour E, Sat E, Grigull J, Pan Q, Peng WT, Krogan N, Greenblatt J, Fehlings M, Kooy van der D, Aubin J, Bruneau BG, Rossant J, Blencowe BJ, Frey BJ, Hughes TR: The functional landscape of mouse gene expression. J Biol. 2004, 3: 21- · DOI · Paper page
  12. Marcotte EM, Pellegrini M, Thompson MJ, Yeates TO, Eisenberg D: A combined algorithm for genome-wide prediction of protein function. Nature. 1999, 402: 83-86. · DOI · Paper page
  13. Myers CL, Robson D, Wible A, Hibbs MA, Chiriac C, Theesfeld CL, Dolinski K, Troyanskaya OG: Discovery of biological networks from diverse functional genomic data. Genome Biol. 2005, 6: R114- · DOI · Paper page
  14. Peña-Castillo L, Tasan M, Myers CL, Lee H, Joshi T, Zhang C, Guan Y, Leone M, Pagnani A, Kyu Kim W, Krumpelman C, Tian W, Obozinski G, Qi Y, Mostafavi S, Ning Lin G, Berriz GF, Gibbons FD, Lanckriet G, Qiu J, Grant C, Barutcuoglu Z, Hill DP, Warde-Farley D, Grouios C, Ray D, Blake JA, Deng M, Jordan MI, Noble WS: A critical assessment of Mus musculus gene function prediction using integrated genomic evidence. Genome Biol. 2008, 9 (Suppl 1): S2- · DOI · Paper page
  15. Sharan R, Ulitsky I, Shamir R: Network-based prediction of protein function. Mol Syst Biol. 2007, 3: 88- · DOI · Paper page
  16. Tsuda K, Shin H, Scholkopf B: Fast protein classification with multiple networks. Bioinformatics. 2005, 21 (Suppl 2): ii59-i65. · DOI · Paper page
  17. von Mering C, Jensen LJ, Kuhn M, Chaffron S, Doerks T, Kruger B, Snel B, Bork P: STRING 7 - recent developments in the integration and prediction of protein interactions. Nucleic Acids Res. 2007, D358-D362. 35 Database · DOI · Paper page
  18. Yona G, Dirks W, Rahman S, Lin DM: Effective similarity measures for expression profiles. Bioinformatics. 2006, 22: 1616-1622. · doi:10.1093/bioinformatics/btl127
  19. Wong SL, Zhang LV, Roth FP: Discovering functional relationships: biochemistry versus genetics. Trends Genet. 2005, 21: 424-427. · DOI
  20. Zhou D, Bousuet O, Lal T, Weston J, Schoelkopf B: Learning with local and global consistency. Neural Information Processing Systems. 2003, [http://books.nips.cc/papers/files/nips16/NIPS2003_AA41.pdf]
  21. Zhu X, Ghahramani Z, Lafferty J: Semi-supervised learning using Gaussian fields and harmonic functions. Proceedings of the Twentieth International Conference on Machine Learning: August 21-24, 2003; Washington, DC, USA. Edited by: Fawcett T, Mishra N. 2003, Menlo Park, CA:AAAI Press, 912-919.
  22. Vazquez A, Flammini A, Maritan A, Vespignani A: Global protein function prediction from protein-protein interaction networks. Nat Biotechnol. 2003, 21: 697-700. · DOI · Paper page
  23. Nabieva E, Jim K, Agarwal A, Chazelle B, Singh M: Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics. 2005, 21 (Suppl 1): i302-310. · DOI · Paper page
  24. Deng M, Chen T, Sun F: An integrated probabilistic model for functional prediction of proteins. J Comput Biol. 2004, 11: 463-475. · DOI · Paper page
  25. Brown MP, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS, Ares M, Haussler D: Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci USA. 2000, 97: 262-267. · DOI · Paper page
  26. Pavlidis P, Weston J, Cai J, Noble WS: Learning gene functional classifications from multiple data types. J Comput Biol. 2002, 9: 401-411. · DOI · Paper page
  27. Lewis DP, Jebara T, Noble WS: Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure. Bioinformatics. 2006, 22: 2753-2760. · DOI
  28. King OD, Foulger RE, Dwight SS, White JV, Roth FP: Predicting gene function from patterns of annotation. Genome Res. 2003, 13: 896-904. · DOI
  29. Zhang T, Popescul A, Dom B: Linear prediction model with graph regularization for web-page categorization. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: August 20-23, 2006. 2006, Philadelphia, PA, USA. New York, NY: ACM, 821-826. · DOI
  30. Gribskov M, Robinson NL: Use of receiver operating characteristic (ROC) analysis to evaluate sequence matching. Comput Chem. 1996, 20: 25-33. · DOI · Paper page
  31. Cristianini N, Elisseef A, Shawe-Taylor J, Kndola J: On kernel-target alignment. Proceedings of Neural Information Processing Systems. 2001, [http://books.nips.cc/papers/files/nips14/LT17.pdf]

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Metadata

Authors
Mostafavi, S., Ray, D., Warde-Farley, D., Grouios, C., & Morris, Q.
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2008-06-27
Volume
9
Issue
S1
Pages
S4-S4
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/gb-2008-9-s1-s4
OpenAlex
W2124649441
Cited by
1,055
References
5
FWCI
6.45
Type
journal article
Language
en
Open access
gold
Licence

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CoChat. (2026). Citation check: GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. https://app.cochat.ai/papers/10.1186/gb-2008-9-s1-s4
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Mostafavi, S., Ray, D., Warde-Farley, D., Grouios, C., & Morris, Q. (2008). GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biology. https://doi.org/10.1186/gb-2008-9-s1-s4

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